Differential uncertainties and risk attitudes between conventional and organic producers: the case of Spanish arable crop farmers

The growing importance of economic factors in farmers' decisions to go organic has raised interest in characterizing the economic behavior of organic versus conventional farms. In general, published analyses so far have not considered differential uncertainties, abilities to control production risk, and farmers' risk preferences between conventional and organic practices when comparing these techniques. Our article attempts to assess this issue. We use a model of farmer decision under risk to analyze the differential values between organic and conventional Spanish arable crop farms and to assess the incentives for adoption of organic practices. Results show that organic and conventional farms do have different production risks as well as different aversions to risk. Organic price premiums and subsidies are found to be powerful instruments to motivate adoption of organic techniques.     

Improving the speed of adoption of agricultural technologies and farm performance through farmer groups: evidence from the Great Lakes region of Africa

The article examines the effect of membership in farmer groups (MFG) on adoption lag of agricultural technologies and farm performance in Burundi, the Democratic Republic of Congo and Rwanda. We use duration and stochastic production frontier models on farm household data. We find that the longer the duration of MFG, the shorter the adoption lag and much more so if combined with extension service delivery. Farmer groups function as an important mechanism for improving farm productivity through reduced technical inefficiency in input use. We discuss policy implications under which farmer groups are a useful channel to reduce adoption lag, and the means through which improved farm performance can be achieved.     

Adoption and Diffusion of Natural-Resource-Conserving Agricultural Technology

A national sample of U.S. farms is used to estimate the long-term trends in adoption and diffusion of conservation tillage, IPM, and soil fertilizer testing, technologies designed to reduce environmental externalities from agriculture. Results from a duration model show that diffusion of these technologies has been relatively slow, with long lags in adoption due to differences in land quality, farm size, farmer education, and regional factors.     

Role of education and extension in the adoption of technology: a study of upland rice and soybean farmers in Central-West Brazil

This paper explores reduced form determinants of the adoption of certain technologies by upland rice and soybean farmers in the Center-West region of Brazil. We merge community level data on the availability and quality of publicly provided infrastructure, principally extension, to the farm level data containing information on farmer human capital as well as land quantity and quality. By using community level measures of availability and quality of extension, we avoid problems of endogeneity of farm level measures of extension use. We find positive impacts of farmer education on the diffusion process, in accordance with other studies. We also isolate effects of the quality in regional extension investment as measured by the average experience of technical extension staff. These results indicate that investments in human capital of extension workers does have a payoff in terms of farmer adoption of improved cultivation practices.     

Early adoption,experience, and farm performance of GM corn seeds

The current article explores the characteristics that distinguish early from late adopters of GM corn and measures the productivity impacts of early adoption, for a sample of farmers in Minnesota and Wisconsin. The results of the adoption analysis confirm that size, education as well as specialization are positively correlated with early adoption. In addition, these results also show that farms that are mostly worked by family labor but hire some off‐farm labor are more likely to adopt GM seeds earlier in the diffusion process. The productivity analysis demonstrates the superiority of stacked varieties. At the same time, we find no evidence of a direct impact of experience on yields. Given the previously documented impact of early adoption on the use of stacked varieties, we conclude that experience plays a role through the adoption of these new technologies but does not play a role in allowing the producer to use the technology more efficiently, once it has been adopted.     

Bias from Farmer Self‐Selection in Genetically Modified Crop Productivity Estimates: Evidence from Indian Data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm‐level yield gains from GM crop adoption. Yet most farm‐level studies in the literature do not control for farmer self‐selection, a potentially important source of bias in such estimates. We use farm‐level panel data from Indian cotton farmers to investigate the yield effect of GM insect‐resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self‐selection. A production function is estimated using a fixed‐effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross‐sectional estimates of the yield effect of Bt cotton, which do not control for self‐selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.     

Sequential Adoption of Site-Specific Technologies and Its Implications for Nitrogen Productivity: A Double Selectivity Model

This paper analyzes the sequential decision to adopt two site-specific technologies, soil testing and variable rate technology, and the impact of adoption on nitrogen productivity. The results indicate that while farm location was a key variable influencing adoption of soil testing, farm size, human capital, and innovativeness of farmers had a significant impact on adoption of variable rate technology in four Midwestern states. A double selectivity model applied to correct for sample selection bias shows that adoption leads to significant gains in nitrogen productivity for farms with below average soil quality but statistically insignificant gains for farms with above average soil quality.     

Econometric analysis of noncompliance with organic farming standards in Switzerland

Applying the economics of crime theory, we model the decision of an opportunistic and/or careless organic farmer and derive hypotheses to explain noncompliance. Where empirical data are available, hypotheses are tested. We use data for the years 2007 through 2009 of organic farms certified by Bio Suisse. Imposed sanctions are used as a proxy variable for noncompliance and farm characteristics as explanatory variables. Random effects logit models show that processing activities and livestock diversity significantly increase a farm's sanction probability. Past noncompliances also indicate a higher present sanction probability. Finally, we discuss some methodological issues and suggest a way to organize risk‐based inspections more effectively.     

Farmers’ objectives as determinants of organic farming adoption: the case of Catalonian vineyard production

This article assesses the decision to adopt organic farming practices. We use Duration Analysis (DA) to determine why farmers adopt organic farming practices and what influences the timing of adoption. We extend previous studies by including farmers’ objectives, risk preferences, and agricultural policies as covariates in the DA model. The Analytical Hierarchy Process is used as a multicriteria decision‐making methodology to measure farmers’ objectives. The empirical analysis uses farm‐level data from a sample of vineyard farms in the Spanish region of Catalonia. Farmers’ objectives are found to influence the conversion decision. Moreover, farmers who are not risk averse are more likely to adopt organic farming. Results point to the policy changes that have been most relevant in motivating adoption of organic practices.     

Comparing economic performance of organic and conventional U.S. beef farms using matching samples*

Economic performance measures of organic and transitioning‐to‐organic cow–calf farms are compared with those of non‐organic cow–calf farms. A method of matching samples is used for the comparison, estimating sample average treatment effects for the subpopulation of the treated. Each organic farm is matched with one non‐organic farm that is involved in the same beef industry segments and farm size classes, and in the same region. Furthermore, farmer demographic, farming system, and technology variables are used to identify matches. Bias is reduced by estimating separate weighted regression functions for the treated and untreated groups. Results suggest that higher cost of organic production is due primarily to higher capital recovery, taxes and insurance, and overhead costs. Evidence is found for differences in beef enterprise profitability by organic status.     

Adoption of precision farming tools: A context-related analysis

This paper deals with precision farming tools (PFTs), a way of farming which relies on specialized equipment, software and information technologies services, whose importance is underlined in recent documents of the European Union. Precision farming is an integrated and sustainable farm management system making use of modern technologies to increase farm’s profitability, by reducing environmental impact. In this paper we explore the complex mechanisms that affect PFT’s adoption by Italian farmers. More precisely, we try to analyse the context-related factors affecting adoption of PFTs in the Italian farms.Little research has been carried out in Italy on this topic, therefore our paper tries to fill a gap in literature. In order to investigate the process of technology adoption related to precision agriculture, a questionnaire was submitted to a sample of Italian farms. The questionnaire has been structured in order to apply the AKAP (Awareness, Knowledge, Adoption, Product) sequence. Our analysis underlines that context-related factors are fundamental dimensions to be explored in order to specify uptake of PFTs. Therefore, the paper has relevant policy implications, within the context of a new participatory approach to agricultural innovation characterized by bottom-up processes boosted by farmers, which has informed the recent policies of agricultural innovation at the EU level.     

Farm Size,Technology Adoption and Agricultural Trade Reform: Evidence from Canada

Using detailed census data covering over 30,000 farms in Alberta, Saskatchewan and Manitoba, Canada, we document the vast and increasing farm size heterogeneity, and analyse the role of farm size in adapting to the removal of an export subsidy in 1995. Consistent with the Alchian-Allen hypothesis, the increase in per-unit trade costs due to the reform was associated with farms of all sizes shifting their production of crops from low value wheat to higher value canola. We find that switching to new labour-saving tillage technologies and away from summerfallow in response to the large negative shock to grain prices caused by the reform varied across the farm size distribution. We develop a theory of heterogenous farms and technology adoption that can explain our findings.     

Effect of yield and price risk on conversion from conventional to organic farming*

Although the benefits of organic farming are already well known, the conversion to organic farming does not proceed as the Dutch government expected. In order to investigate the conversion decisions of Dutch arable farms, a discrete stochastic dynamic utility‐efficient programming (DUEP) model is developed with special attention for yield and price risk of conventional, conversion and organic crops. The model maximizes the expected utility of the farmer depending on the farmer’s risk attitude. The DUEP model is an extension of a dynamic linear programming model that maximized the labour income of conversion from conventional to organic farming over a 10 year planning horizon. The DUEP model was used to model a typical farm for the central clay region in the Netherlands. The results show that for a risk‐neutral farmer it is optimal to convert to organic farming. However, for a more risk‐averse farmer it is only optimal to fully convert if policy incentives are applied such as taxes on pesticides or subsidies on conversion, or if the market for the organic products becomes more stable.     

The impact of decoupled payments on U.S. cropland values

An extensive literature has shown that various farm programs may influence the value of farmland, but other studies have not examined the cropland price effects of direct, or decoupled, payments separate from countercyclical payments. This study uses nationally representative confidential field‐level panel data with farmer‐reported per‐acre land values. We analyze the impact of decoupled and other farm program payments on farmland values. Using a fixed effects model and controlling for various factors that influence farmland values, we find that an additional dollar of decoupled payments has a large and statistically significant impact on farmland values of about $18 per acre. These results are comparable with similar studies undertaken in Europe.     

Innovation and networks in New Zealand farming

The New Zealand government seeks to dramatically increase the value of agricultural exports while concurrently protecting the natural environment. Thus, farmers are expected to adopt pro‐environmental management practices and novel farm technologies. We show that farmers are more likely to adopt new practices and technologies after seeing them demonstrated, but earlier evidence indicates that demonstration is most effectively undertaken within farmer networks. We use multivariate regression to identify the traits of livestock farmers who are innovative by focusing on adoption of pro‐environmental management practices (managing nutrients, soils and pugging) and novel farm technologies (e.g., windmills, computer‐based management systems, automatic sensors and specialised grasses), considering both numbers adopted and timing. We find that dairy farmers are more innovative than other livestock farmers and that higher education levels and stronger environmental norms within the family are strongly associated with innovativeness. In addition, we find that innovators and early adopters have larger networks than other farmers. Moreover, the composition of these networks is much more varied than the networks of less innovative farmers. These findings imply that innovative farmers in New Zealand may also act as connectors for the diffusion of new ideas in farming.     

When my neighbors matter: Spillover effects in the adoption of large-scale pesticide-free wheat production

We investigate the spillover effects in farmers’ adoption decisions of a novel pesticide-free wheat production system. To this end, we exploit the variability and asymmetry in the social ties among neighboring farmers. We find evidence of spillover effects in farmers’ adoption decisions as well as in farm and farmer characteristics. Our results further highlight the importance of accounting for potentially heterogeneous social ties in farmer networks beyond pure measures of spatial proximity: spillover effects are only robust once we account for the strength of social ties through farmers’ stated tendency to consult peers on agricultural decisions. Our findings highlight the relevance of peer influence in the diffusion of sustainable agriculture practices even in contexts of well-functioned institutions and high interest in environmental protection such as European agriculture. We discuss implications for the design of policies and programs for sustainable agriculture, which are currently in the center of attention in agricultural policymaking.     

The impact of integrated aquaculture–agriculture on small-scale farms in Southern Malawi

Sustainable agricultural intensification is an urgent challenge for Sub-Saharan Africa. One potential solution is to rely on local farmers' knowledge for improved management of diverse on-farm resources and integration among various farm enterprises. In this article, we analyze the farm-level impact of one recent example, namely the integrated aquaculture–agriculture (IAA) technologies that have been developed and disseminated in a participatory manner in Malawi. Based on a 2004 survey of 315 respondents (166 adopters and 149 nonadopters), we test the hypothesis that adoption of IAA is associated with improved farm productivity and more efficient use of resources. Estimating a technical inefficiency function shows that IAA farms were significantly more efficient compared to nonadopters. IAA farms also had higher total factor productivity, higher farm income per hectare, and higher returns to family labor.     

The influence of direct payments on farmers’ hail insurance decisions

We analyze determinants of hail insurance use of Swiss farmers, using FADN panel data covering the period 1990–2009. Mixed effect logistic regression models are estimated to identify the most important farm and farmer characteristics that trigger insurance use. In addition, information on local hail risk is taken into account in these models. It shows that larger farms, with specialization in crop production, and with larger local hail risks are more likely to adopt the hail insurance. Moreover, insurance users are usually older and better educated. Since the early 1990s, Swiss agricultural policy has reduced price support and introduced general and ecological direct payments. This has led to a much higher importance of direct payments for farmers’ incomes. Our analysis shows that this development has contributed to decreasing hail insurance adoption rates in Switzerland over the period considered. Our results indicate that the larger the share of direct payments for total farm revenue, the less attractive is insurance as a risk management strategy for farmers. This interdependency should be explicitly considered by agricultural policy in the design of support mechanisms.     

Enhancing productivity on suburban dairy farms in China

Dairy farms in China's suburban areas have been playing an important role in providing urban markets with fresh milk. With the rising demand for fluid milk and dairy products in the cities, there is a perception that small and scattered dairy farms in China's provinces are gradually disappearing and more concentrated dairy cattle farming is being formed near suburban areas. This article uses farm‐level survey data and stochastic input distance functions to make estimates of total factor productivity (TFP) on suburban dairy farms, as well as for the entire dairy sector. The results show that over the past decade TFP growth has been positive on suburban dairy farms, and this rise in productivity has been driven mostly by technological change. However, at the same time we find that, on average, the same farms have been falling behind the advancing technical frontier. We also find one of the drivers of the suburban dairy sector is the relatively robust rate of technological change of these farms, which has been more rapid than on farms in the dairy sector as a whole. The results suggest that efforts to achieve greater adoption of new technologies and better advice on how to use the technologies and manage production and marketing within the suburban dairy sector will further advance productivity growth in the sector.     

Incorporating risk in a positive mathematical programming framework: a dual approach

In this study we develop a new methodological proposal to incorporate risk into a farm‐level positive mathematical programming (PMP) model. We estimate simultaneously the farm nonlinear cost function and a farmer‐specific coefficient of absolute risk aversion as well as the resource shadow prices. The model is applied to a sample of representative arable crop farms from the Emilia‐Romagna region in Italy. The estimation results confirm the calibration ability of the model and reveal the values of the individual risk aversion coefficients. We use the model to simulate different scenarios of crop price volatility, in order to explore the potential risk management role of an agri‐environmental scheme.